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Efficient Multi-User Computation Offloading for Mobile-Edge Cloud Computing

arXiv 1510.00888 ↗First posted 4 Oct 2015cs.NIcited by 28

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Abstract (arXiv)

Mobile-edge cloud computing is a new paradigm to provide cloud computing capabilities at the edge of pervasive radio access networks in close proximity to mobile users. In this paper, we first study the multi-user computation offloading problem for mobile-edge cloud computing in a multi-channel wireless interference environment. We show that it is NP-hard to compute a centralized optimal solution, and hence adopt a game theoretic approach for achieving efficient computation offloading in a distributed manner. We formulate the distributed computation offloading decision making problem among mobile device users as a multi-user computation offloading game. We analyze the structural property of the game and show that the game admits a Nash equilibrium and possesses the finite improvement property. We then design a distributed computation offloading algorithm that can achieve a Nash equilibrium, derive the upper bound of the convergence time, and quantify its efficiency ratio over the centralized optimal solutions in terms of two important performance metrics. We further extend our study to the scenario of multi-user computation offloading in the multi-channel wireless contention environment. Numerical results corroborate that the proposed algorithm can achieve superior computation offloading performance and scale well as the user size increases.

Key facts. Cited by 28 papers in the map (data through 14 Oct 2026). First posted on arXiv 4 Oct 2015.

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Citing papers per year: 2021: 4; 2022: 10; 2023: 3; 2024: 3; 2025: 6; 2026: 2.

28 papers.

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  1. 2022Multi-Agent Collaborative Inference via DNN Decoupling: Intermediate Feature Compression and Edge Learningcited by 12
  2. 2021Computational Intelligence and Deep Learning for Next-Generation Edge-Enabled Industrial IoTcited by 9
  3. 2022Resource Allocation for Mobile Metaverse with the Internet of Vehicles over 6G Wireless Communications: A Deep Reinforcement Learning Approachcited by 6
  4. 2024Model Partition and Resource Allocation for Split Learning in Vehicular Edge Networkscited by 6
  5. 2021Decentralized Task Offloading in Edge Computing: A Multi-User Multi-Armed Bandit Approachcited by 5
  6. 2022Multi-Agent Distributed Reinforcement Learning for Making Decentralized Offloading Decisionscited by 4
  7. 2021Tailored Learning-Based Scheduling for Kubernetes-Oriented Edge-Cloud Systemcited by 3
  8. 2022QoS-SLA-Aware Adaptive Genetic Algorithm for Multi-Request Offloading in Integrated Edge-Cloud Computing in Internet of Vehiclescited by 3
  9. 2023Energy-Efficient Downlink Semantic Generative Communication with Text-to-Image Generatorscited by 3
  10. 2025Enhancing Disaster Resilience with UAV-Assisted Edge Computing: A Reinforcement Learning Approach to Managing Heterogeneous Edge Devicescited by 2
  11. 2022MSCET: A Multi-Scenario Offloading Schedule for Biomedical Data Processing and Analysis in Cloud-Edge-Terminal Collaborative Vehicular Networkscited by 1
  12. 2022Distributed Task Management in Fog Computing: A Socially Concave Bandit Gamecited by 1
16 more
  1. 2023Dynamic Partial Computation Offloading for the Metaverse in In-Network Computingcited by 1
  2. 2021DeepEdge: A Deep Reinforcement Learning based Task Orchestrator for Edge Computingcited by 0
  3. 2022Monitoring and Proactive Management of QoS Levels in Pervasive Applicationscited by 0
  4. 2022Multi-Agent Reinforcement Learning for Long-Term Network Resource Allocation through Auction: a V2X Applicationcited by 0
  5. 2022FIRE: A Failure-Adaptive Reinforcement Learning Framework for Edge Computing Migrationscited by 0
  6. 2022Beyond 5G Networks: Integration of Communication, Computing, Caching, and Controlcited by 0
  7. 2023Quantum-Based Feature Selection for Multi-classification Problem in Complex Systems with Edge Computingcited by 0
  8. 2024DeepAir: A Multi-Agent Deep Reinforcement Learning Based Scheme for an Unknown User Location Problemcited by 0
  9. 2024Tera-SpaceCom: GNN-based Deep Reinforcement Learning for Joint Resource Allocation and Task Offloading in TeraHertz Band Space Networkscited by 0
  10. 2025MetaFed: Advancing Privacy, Performance, and Sustainability in Federated Metaverse Systemscited by 0
  11. 2025Federated Multi-Agent Reinforcement Learning for Privacy-Preserving and Energy-Aware Resource Management in 6G Edge Networkscited by 0
  12. 2025Enhancing Time-Series Anomaly Detection by Integrating Spectral-Residual Bottom-Up Attention with Reservoir Computingcited by 0
  13. 2025A Hybrid Proactive And Predictive Framework For Edge Cloud Resource Managementcited by 0
  14. 2025Evaluating Device-First Continuum AI (DFC-AI) for Autonomous Operations in the Energy Sectorcited by 0
  15. 2026TimeGNN-Augmented Hybrid-Action MARL for Fine-Grained Task Partitioning and Energy-Aware Offloading in MECcited by 0
  16. 2026Entropy-Aware Task Offloading in Mobile Edge Computingcited by 0